title: Fuzzy C-means clustering and principal component analysis of time series from near-infrared imaging of forearm ischemia.
abstract: Fuzzy C-means clustering and principal components analysis were used to analyze a temporal series of near-IR images taken of a human forearm during periods of venous outflow restriction and complete forearm ischemia. The principal component eigen-time course analysis provided no useful information and the principal component eigen-image analysis gave results that correlated poorly with anatomical features. The fuzzy C-means clustering analysis, on the other hand, showed distinct regional differences in the hemodynamic response and scattering properties of the tissue, which correlated well with the anatomical features of the forearm.
authors.affiliation: Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba.
authors.forename: J R
authors.initials: JR
authors.lastname: Mansfield
authors.forename: M G
authors.initials: MG
authors.lastname: Sowa
authors.forename: G B
authors.initials: GB
authors.lastname: Scarth
authors.forename: R L
authors.initials: RL
authors.lastname: Somorjai
authors.forename: H H
authors.initials: HH
authors.lastname: Mantsch
mesh_terms: D016000:Cluster Analysis; D005542:Forearm / Q000098:blood supply*; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007511:Ischemia / Q000175:diagnosis*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*